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Pixel level spatial variability modeling using SHAP reveals the relative importance of factors influencing LST

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Abstract

As an important indicator of the regional thermal environment, land surface temperature (LST) is closely related to community health and regional sustainability in general, and is influenced by multiple factors. Previous studies have paid scant attention to spatial heterogeneity in the relative contribution of factors underlying LST. In this study of Zhejiang Province, we investigated the key factors affecting daytime and nighttime annual mean LST and the spatial distribution of their respective contributions. The eXtreme Gradient Boosting tree (XGBoost) and Shapley Additive exPlanations algorithm (SHAP) approach were used in combination with three sampling strategies (Province—Urban Agglomeration -Gradients within Urban Agglomeration) to detect spatial variation. The results reveal heterogenous LST spatial distribution with lower LST in the southwestern mountainous region and higher temperatures in the urban center. Spatially explicit SHAP maps indicate that latitude and longitude (geographical locations) are the most important factors at the provincial level. In urban agglomerations, factors associated with elevation and nightlight are shown to positively impact daytime LST in lower altitude regions. In the urban centers, EVI and MNDWI are the most notable influencing factors on LST at night. Under different sampling strategies, EVI, MNDWI, NL, and NDBI affect LST more prominently at smaller spatial scales as compared to AOD, latitude and TOP. The SHAP method proposed in this paper offers a useful means for management authorities in addressing LST in a warming climate.

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Acknowledgements

We sincerely thank the editor and anonymous reviewers for their valuable comments and suggestions to improve the quality of this paper.

Funding

This research was funded by the Natural Science Foundation of Zhejiang Province (NO. LQ19D010007), Jinhua Science and Technology Research Program (NO. 2021–4-341) and Independent Design Scientific Research Project of Zhejiang Normal University (NO. 2021ZS0702).

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Authors and Affiliations

  1. College of Geography and Environmental Sciences, Zhejiang Normal University, Jinhua, 321004, China

    Yuhong Hu, Chaofan Wu & Michael E. Meadows

  2. Department of Environmental and Geographical Science, University of Cape Town, Cape Town, 7700, South Africa

    Michael E. Meadows

  3. School of Geography and Ocean Sciences, Nanjing University, Nanjing, 210023, China

    Michael E. Meadows

  4. School of Geographical Sciences, University of Nottingham Ningbo China, Ningbo, 315100, China

    Meili Feng

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  3. Michael E. Meadows
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  4. Meili Feng
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Contributions

Hu Yuhong: conceptualization, methodology, validation, writing—original draft preparation, and software. Wu Chaofan: conceptualization, writing—original draft preparation, writing—review and editing, and funding acquisition. Michael E. Meadows: writing—review and editing. Feng Meili: writing—review and editing. All authors reviewed the manuscript.

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Correspondence to Chaofan Wu.

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Hu, Y., Wu, C., Meadows, M.E. et al. Pixel level spatial variability modeling using SHAP reveals the relative importance of factors influencing LST. Environ Monit Assess 195, 407 (2023). https://doi.org/10.1007/s10661-023-10950-2

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